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• Open Access   Article

  Necessity and efficacy of Self-learning during Covid-19 Pandemic

  Ninad Marathe, Rohan Nikam

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.5 , pp.1-6, Oct-2021

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  Abstract
  Self-learning is the process when someone is trying to gather information and process it on their own. It is the modern way of improving one`s own skills or learning something new on their own. Many people do it in their own interest while some have to do it because of the need of the situation. Because of the Covid-19 pandemic, the traditional teaching methods cannot be followed leading students to learn on their own. In this paper, we are going to discuss the necessity and effectiveness among students related to Self-Learning before the pandemic and after the pandemic. Data was gathered by performing surveys where students have answered some choice-based questions, that will help us with our analysis.

  Key-Words / Index Term
  Self-learning, Covid-19, R- programming, logistic regression, ggplot, data cleaning, correlation

  References
  [1] Deepansh Sharma, Gulsimar Kaur, Himanshu Arora, Ishan Sharma, Jyoti Diwakar, Muskan Jain “Comprehensive Analysis of Different Regression Techniques and its Applications used in Machine Learning”, International Research Journal of Engineering and Technology (IRJET) Volume: 07 Issue:07 July 2020.
  [2] V.Lakshmi, K.Harika, H.Bhavishya, Ch. Sri Harsha “SENTIMENT ANALYSIS OF TWITTER DATA” International Research Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 02 Feb -2017.
  [3] Navid Aghdaei, Georgios Kokogiannakis, Daniel Daly, Timothy McCarthy, “Linear Regression models for prediction of annual heating and cooling demand in representative Australian residential dwellings”, International Conference on Improving Residential Energy Efficiency, IREE 2017, ppno: 79- 86, 2017.
  [4] Macedo Maia; Andrea Freitas; Siegfried Handschuh, FinSSLx: “A Sentiment Analysis Model for the Financial Domain Using Text Simplification”, 2018 IEEE 12th International Conference on Semantic Computing (ICSC) 2 Feb. 2018.
  [5] Di Renzo L, Gualtieri P, Pivari F, Soldati L, Attinà A, Cinelli G, et al. “Eating habits and lifestyle changes during COVID-19 lockdown: an Italian survey”. J Transl Med. 2020;18(1):229. pmid:32513197.
  [6] Palvia S, Aeron P, Gupta P, Mahapatra D, Parida R, Rosner R, et al. Online Education: Worldwide Status, Challenges, Trends, and Implications. Journal of Global Information Technology Management; Volume 21(4): 233–241. 2018.
  [7] Rizun M, Strzelecki A. Student’ acceptance of the COVID-19 impact on shifting higher education to distance learning in Poland. Int J Environ Res Public Health. 2020;17(18): 6468.
  [8] Jeffery, K. A., and Bauer, C. F. Students’ responses to emergency remote online teaching reveal critical factors for all teaching. J. Chem. Educ. 97: 2472–2485. 2020.
  [9] Li, L. Y., & Tsai, C. C. Accessing online learning material: Quantitative behavior patterns and their effects on motivation and learning performance. Computers and Education, 114, 286–297.2017.
  [10] Lubis, M.. Peluang Pemanfaatan Pembelajaran Berorientasi Teknologi Informasi di Lingkup Madrasah (Mempersiapkan Madrasah Berwawasan Global). Tadris: Jurnal Keguruan Dan Ilmu Tarbiyah, 1(2), 147–153. 2016.
  [11] Mandusic, D., & Blaskovic, L.” The impact of collaborative learning on critical thinking”. Trakia Journal of Science, 13(1), 426–428. 2015.
  [12] Nguyen T. “The Effectiveness of Online Learning: Beyond No Significant Difference and Future Horizons”. MERLOT Journal of Online Learning and Teaching, 11(2), 309. 2015.

  Citation
  Ninad Marathe, Rohan Nikam, "Necessity and efficacy of Self-learning during Covid-19 Pandemic," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.5, pp.1-6, 2021

• Open Access   Article

  Optimal Path Planning for Navigation Using a Generalized Genetic Algorithm

  Ramya S.

  Review Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.5 , pp.7-13, Oct-2021

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  Abstract
  Finding an optimal collision-free route from an arbitrary point to a designated point with obstacles in a given environment is essential to design an algorithm for path planning. The main goal is to search a workspace to find the shortest path within a minimal amount of time, by starting at the source point and exploring adjacent objects until the destination point is reached. This paper gives an overview of path planning algorithms like A*, D star, PRM, and RRT, which gives an optimal path to find the way in a given workspace. This paper also presents how to coalesce the Genetic Algorithm with other path planning algorithms to find an optimal path in a given workspace.

  Key-Words / Index Term
  A star search algorithm, D star algorithm, Probabilistic Road Mapping, Rapidly-exploring Random Tree, and Genetic Algorithm

  References
  [1]. M. Kurant, A. Markopoulou and P. Thiran, "On the bias of BFS (Breadth First Search)," 2010 22nd International Teletraffic Congress (lTC 22), Amsterdam, Vol: 5, pp. 1-8, doi: 10.1109/ITC.2010.5608727,2010.
  [2]. B. Swami and R. Singh, "Performance analysis of DFS based ordered walk learning routing protocol in MANET," 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), Noida, Vol: 3, pp. 195-198, doi: 10.1109/ICGCIoT.2015.7380456,2015.
  [3].M. Nazarifard and D. Bahrepour, "Efficient implementation of the Bellman-Ford algorithm on GPU," 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI), Tehran, Vol: 2, pp. 0773-0778, doi: 10.1109/KBEI.2017.8324901,2017.
  [4]. N. Makariye, "Towards shortest path computation using Dijkstra algorithm," 2017 International Conference on IoT and Application (ICIOT), Nagapattinam, Vol: 6, pp. 1-3, doi: 10.1109/ICIOTA.2017.8073641, 2017.
  [5]. J. Yao, C. Lin, X. Xie, A. J. Wang and C. Hung, "Path Planning for Virtual Human Motion Using Improved A* Star Algorithm," 2010 Seventh International Conference on Information Technology: New Generations, Las Vegas, NV, 2010, pp. 1154-1158, doi: 10.1109/ITNG.2010.53.
  [6]. Xiaowu Liu, Riqiang Deng, Jinwen Wang, Xunzhang Wang, COStar: A D-star Lite-based dynamic search algorithm for codon optimization, Journal of Theoretical Biology, Volume 344, 2014, Pages 19-30, ISSN 0022-5193.
  [7]. Sigurd Lazic Villumsen, Morten Kristiansen, PRM Based Motion Planning for Sequencing of Remote Laser Processing Tasks, Procedia Manufacturing, Volume 11, 2017, Pages 300-310, ISSN 2351-9789.
  [8]. R. Kala, A. Shukla, R. Tiwari (2011) Robotic path planning in static environment using hierarchical multineuron heuristic search and probability based fitness, Neurocomputing, 74(14-15), 2314-2335..
  [9]. Xiaoman Cao, Xiangjun Zou, Chunyang Jia, Mingyou Chen, Zeqin Zeng, RRT-based path planning for an intelligent litchi-picking manipulator, Computers and Electronics in Agriculture, Volume 156, 2019, Pages 105-118, ISSN 0168-1699.
  [10]. S. Doostie, A.K. Hoshiar, M. Nazarahari, Seungmin Lee, Hongsoo Choi, Optimal path planning of multiple nanoparticles in continuous environment using a novel Adaptive Genetic Algorithm, Precision Engineering,Volume 53, 2018, Pages 65-78, ISSN 0141-6359.
  [11]. Gasparetto A., Boscariol P., Lanzutti A., Vidoni R. (2015) Path Planning and Trajectory Planning Algorithms: A General Overview. In: Carbone G., Gomez-Bravo F. (eds) Motion and Operation Planning of Robotic Systems. Mechanisms and Machine Science, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-319-14705-5_1

  Citation
  Ramya S., "Optimal Path Planning for Navigation Using a Generalized Genetic Algorithm," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.5, pp.7-13, 2021

• Open Access   Article

  Library Management Information System Based On Automatic Identifier QR Code and NFC

  I. Gede Sujana Eka Putra, I. Made Tirta Mahayana, I.G. Agung Wicaksono Dharmayasa

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.5 , pp.14-19, Oct-2021

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  Abstract
  Radio Frequency Identification as one of the commercial compact wireless technologies has been implemented in the field of library management in the form of a library management system. RFID apply automation in example inventory control in identifying books in the library. RFID technology is widely used to help problems relating to the indexing of an object such as the identification of books on library, identification of membership or tracking for recording books. In terms of identifying an object, the library has implemented a process book identification using magnetic-based smartcards for self-service purposes. The library to manage books circulations, requires technology that enable to identify objects simultaneously without direct contact required. Various problems that faced by libraries, including the lack of guarantees for the safety of books in the library, human resources library services are not yet optimal, book information availability accuracy. Based on that problem, this research conducts development of library management information system using automatic identifier utilizes RFID and QR Code technology. Each member information stored in NFC card and each book information stored in QR Code label. Transaction of book loan and book return are carried out by tapping the NFC card and scanning the QR Code label for each book. Through the use of NFC Card and QR Code, library provide more practical on book loan transaction and returned book transaction, faster in service and more accurate information, improve library services and provide added value for library.

  Key-Words / Index Term
  Library; Automatic Identifier; QR Code; RF ID; Inventory Control

  References
  [1] R. Habibi Muhammad and R. Satrio Adi, “Rancang Bangun Sistem Pengamanan Mobil Menggunakan ID Card Dengan Metode Radio Frequency Identification,” KOPERTIP Journal Ilmiah Manajemen Informatika dan Komputer., 2017, doi: 10.32485/kopertip.v1i1.10.
  [2] Hidajanto Djamal, “Radio Frequency Identification (RFID) Dan Aplikasinya,” Jurnal Teknik Elektro, vol. 16, no. 1, pp. 45–55, 2014.
  [3] M. Rifqi and N. Wardhani, “Aplikasi Peran Dan Kegunaan Teknologi Near Field Communication (Nfc) Terhadap Kegiatan Proses Belajar Mengajar Di Perguruan Tinggi,” Jurnal Ilmu Teknologi dan Komputer, vol. 1, no. 1, pp. 20–26, 2017.
  [4] Tirta Mahayana, I Gede Sujana Eka Putra, Anthony Lee and I. G. A. W. Dharmayasa, “Design and Development Of Lecturer Attendance System Using Radio Frequency Identification (RFID),” International Journal Computer Science Engineering., vol. 10, no. 1, [Online]. Available: http://www.ijcse.net/docs/IJCSE21-10-01-010.pdf.
  [5] M. P. Lukman and H. Angriani, “Implementasi Teknologi RFID Pada Sistem Antrian Rekam Medis Pasien di Rumah Sakit,” Ilkom Jurnal Ilmiah., 2018, doi: 10.33096/ilkom.v10i1.246.105-112.
  [6] Eko Budi Setiawan, Bobi Kurniawan, “Perancangan Sistem Absensi Kehadiran Perkuliahan Dengan Menggunakan Radio Frequency Identification (RFID),” vol. 1 No 2, 2015.
  [7] J. Onibala, A. S. . Lumenta, and B. A. Sugiarso, “Perancangan Radio Frequency Identification (RFID) Untuk Sistem Absensi Berbasis Mikrokontroler ATmega 8535,” E-Journal Teknik Elektro dan Komputer, 2015.
  [8] W. Wagino, M. E. Rosadi, and A. Wahyudi, “Aplikasi Perpustakaan Menggunakan SmartCard RFID (Radio Frequency Identification),” Technologia, vol. 8, no. 1, 2017, doi: 10.31602/tji.v8i1.717.
  [9] A. Primadhasa, D. Triyanto, and Suhardi, “Sistem Manajemen Perpustakaan Menggunakan Radio Frequency Identification ( Rfid ),” Coding, Sistem Komputer. Untan, vol. 05, no. 3, pp. 32–39, 2017.
  [10] Rifki Maulana Insan and R. Rizal, “Penerapan Teknologi Radio Frequency Identification ( RFID ) Pada Data Kunjungan Perpustakaan,” Journal of Informatics And Digital Expert, vol. 01, pp. 1–6, 2019.
  [11] Sely Yoanda, “Peningkatan Layanan Perpustakaan Melalui Teknologi RFID,” Jurnal Pustakawan Indonesia., vol. 16, no. 2, 2017.
  [12] Manideep Goud V, Srinivas Rao,"Smart Car Parking System based on RFID," International Journal of Computer Sciences and Engineering, vol.7 Issue 8, Aug 2019.
  [13] Hemant Kumar Soni, "Implementation of Raspberry functionality with RFID," International Journal of Scientific Research in Network Security and Communication, Vol.7, Issue.3, pp.40-42, 2019

  Citation
  I. Gede Sujana Eka Putra, I. Made Tirta Mahayana, I.G. Agung Wicaksono Dharmayasa, "Library Management Information System Based On Automatic Identifier QR Code and NFC," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.5, pp.14-19, 2021

• Open Access   Article

  Handwritten Dzongkha Alphabet Recognition System using Convolutional Neural Network

  Deewas Chamling, Yeshi Jamtsho, Yonten Jamtsho

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.5 , pp.20-24, Oct-2021

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  Abstract
  Pattern recognition is one of the fields in computer vision. With the advancement of deep learning technology, many machine learning algorithms were deployed for classification problems. Optical Character Recognition (OCR) is a method of processing and recognizing a character from a handwritten character or a printed document within a digital image. In this paper, an implementation using Convolutional Neural Network (CNN) was proposed for the classification of Handwritten Dzongkha alphabets. The dataset consists of 30 classes, each representing an alphabet of the Dzongkha language with 500 images in each class amounting to a total of 15000 images. Four layered CNN with a kernel size of 3 produced the optimal result in building the model and achieved an accuracy of 97.22% and a loss of 17.62%. This research is carried out for the first time in Bhutan and the findings from the study will act as the benchmark for future researchers. In the future, more handwritten alphabets need to be collected and trained with pre-trained models to get better accuracy.

  Key-Words / Index Term
  OCR; Deep Learning; CNN; Pattern recognition; Dzongkha language

  References
  [1] Vidushi and M. Agarwal, “Intelligent Handwritten Digit Recognition Based on Multiple Parameters using CNN,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.636–641, 2019.
  [2] M. S. K. Dasari, “Optical Character Recognition of Devanagari Script Using Machine Learning- A Survey,” Journal of Xi’an University of Architecture & Technology, Vol. 12, Issue.8, pp.593–599, 2020.
  [3] D. Yadav, S. Sanchez-Cuadrado, and J. Morato, “Optical Character Recognition for Hindi Language Using a Neural-network Approach,” J. Inf. Process. Syst., Vol.9, Issue.1, pp. 117–140, 2013.
  [4] M. Avadesh and N. Goyal, “Optical Character Recognition for Sanskrit Using Convolution Neural Networks,” in 2018 13th IAPR International Workshop on Document Analysis Systems (DAS), pp. 447–452, 2018.
  [5] H. Yadav and S. Jain, “Offline Handwritten Character Recognition using Neural Networks,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.838–845, 2019.
  [6] Y. A. Nanehkaran, D. Zhang, S. Salimi, J. Chen, Y. Tian, and N. Al-Nabhan, “Analysis and comparison of machine learning classifiers and deep neural networks techniques for recognition of Farsi handwritten digits,” J. Supercomput., Vol.77, Issue.4, pp.3193–3222, 2021.
  [7] Y. Bengio, “Practical Recommendations for Gradient-Based Training of Deep Architectures,” in Neural Networks: Tricks of the Trade: Second Edition, G. Montavon, G. B. Orr, and K.-R. Müller, Eds. Berlin, Heidelberg: Springer, pp.437–478, 2012.
  [8] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res., Vol.15, Issue.56, pp.1929–1958, 2014.
  [9] O. K. Oyedotun, A. E. R. Shabayek, D. Aouada, and B. Ottersten, “Going Deeper With Neural Networks Without Skip Connections,” in 2020 IEEE International Conference on Image Processing (ICIP), pp.1756–1760, 2020.
  [10] D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” ArXiv14126980 Cs, Jan. 2017, Accessed: Aug. 13, 2021. [Online]. Available: http://arxiv.org/abs/1412.6980
  [11] R. Poojary and A. Pai, “Comparative Study of Model Optimization Techniques in Fine-Tuned CNN Models,” in 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA), pp.1–4, 2019.

  Citation
  Deewas Chamling, Yeshi Jamtsho, Yonten Jamtsho, "Handwritten Dzongkha Alphabet Recognition System using Convolutional Neural Network," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.5, pp.20-24, 2021

• Open Access   Article

  Examining the Best Speech-to-Text Method for Audio Files in Podcasting

  Siya Naik, Avina Almeida, Shubham Lotliker

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.5 , pp.25-29, Oct-2021

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  Abstract
  Podcasting is a great way to give insights or opinions on any topic to the audience. Podcasting requires both parties to be present physically at the location. But due to the pandemic crisis, this has caused a big problem. So, it is now carried out on an online platform. But the cons are the presence of noise in the audio files as well as miscommunication. The Spectral Gating method is used to remove the noise. This paper compares the various algorithms for converting audio to text by using various speech-to-text pretrained models. We performed an experiment on various audio files and the best accuracy rate was obtained for SpeechRecognition pretrained model.

  Key-Words / Index Term
  Podcast; Subtitles; Spectral Gating; Speech-to-Text; Silero; Vosk; Mozilla DeepSpeech; SpeechRecognition; Word Error Rate; Accuracy.

  References
  [1] Akhil Kanade, Sourabh Gune, Shubham Dharamkar, Rohan Gokhale, “Automatic Subtile Generation for Videos,” Interntional Journal of Enginneering Research and General Science, Vol.3, Issue.6, p.744,2015.
  [2] Siya Sadashiv Naik, Gouri Bhatikar and Ugam Gaude, “Analysis of Best Algorithm for Noise Reduction in Podcasting,” Internatioonal Journal of Scientific Research in Science and Technology, Vol.8, Issue.3, pp24-249,2021.
  [3] N Usha Rani, P N Girija, “Error Analysis to Improve the Speech Recogntion Accuracy on Telegu Language,” Indian Academic of Sciences, Vol.37.Part.6, p.747,2012.
  [4] Aashish Agarwal, Torsten Zesch, “German End-to-end Speech Recognition based on DeepSpeech,” ResearchGate, Germany, Germany, pp.2-3, 2019.
  [5] N. SelvaKumar, M. Rohini, C. Narmada, M. Yogeshprabhu, “Network Traffic Control Using AI,” International Journal of Scientific Research in Network Security and Communication, Vol.8, Issue.2, pp.13-21,2020.
  [6] Muhammad Hafida Firmansyah, Anand Paul, Deblina Bhattachrya, Gul Malik Urfa, “A.I. based Emedded Speech to Text using DeepSpeech,” ResearchGate, South Korea, pp.1-5,2020.
  [7] Dhara Bhatt, Bhargavi Khrishna, “Computer Assisted Pronounciation Learning System Using Speech Recognition Systems “PROnunciation Application”,” International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.6,pp.36-39,2019.

  Citation
  Siya Naik, Avina Almeida, Shubham Lotliker, "Examining the Best Speech-to-Text Method for Audio Files in Podcasting," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.5, pp.25-29, 2021

• Open Access   Article

  A Review on Community Detection Algorithms in Social Networks for COVID-19 Related Fake News Detection, Management and Service Recommendation.

  Ranjith K., Dhiya K.K.

  Review Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.5 , pp.30-37, Oct-2021

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  Abstract
  Social networks play a major role in information sharing than traditional information spreading since most people are active in social networking sites. We know, social platforms bring families and friends together regardless of their location. Community detection in social networks is very useful in offering customized services across communities. Though social platforms are useful in information sharing, it has been misused for fake news spreading as well. Studies show there is a steep rise in COVID-19 related misinformation spread through the internet community during the pandemic which affect people seriously. Therefore, an efficient technique is required to detect and manage this fake news and direct people by providing reliable news and services. In this paper, we review popular community analysis algorithms to build communities based on the user’s age and infection status to deliver reliable news, thereby preventing adverse effects of spreading COVID-19 related misinformation. Based on our observation, we propose a four-step process to validate the authenticity of the information shared on social media. In addition to this, the proposed system is extended to offer other reliable services to communities such as customized shopping recommendations, music recommendations, etc to each community based on participants’ behaviour. This would be helpful for them during lockdown, quarantine or isolation.

  Key-Words / Index Term
  Community; Covid-19; Social networks; Fake news; Recommendations

  References
  [1] Sensis, “Sensis Social Media Report 2016: How Australian People and Businesses are Using Social Media”, Sensis, Vol.1, Issue.1, pp.1-71, 2016.
  [2] Noha Alduaiji, Amitava Datta, and Jianxin Li, “Influence Propagation Model for Clique-Based Community Detection in Social Networks”, IEEE Transactions on Computational Social Systems, Vol. 5, Issue.2, pp.563-575, 2018.
  [3] Junghoon Kim, Tao Guo, Kaiyu Feng, Gao Cong, Arijit Khan, and Farhana M. Choudhury, “Densely Connected User Community and Location Cluster Search in Location-Based Social Networks”, In the proceedings of the 2020 ACM SIGMOD Int’l Conference on Management of Data, Portland, pp.2199-2209, 2020.
  [4] Ke Gu, Dianxing Liu, and Keming Wang, “Social Community Detection Scheme Based on Social-Aware in Mobile Social Networks”, IEEE Access, Vol. 7, pp.173407-173418, 2019.
  [5] Xuemei You, Yinghong Ma and Zhiyuan Liu, “A three-stage algorithm on community detection in social networks”, Knowledge Based Systems, Vol. 187, Issue. 104822, pp.1-22, 2020.
  [6] Xiaohui Pan, Guiqiong Xu, Bing Wang, and Tao Zhang, “A Novel Community Detection Algorithm Based on Local Similarity of Clustering Coefficient in Social Networks”, IEEE Access, Vol. 7, pp.121586-121598, 2019.
  [7] Hui Jiang, Linjuan Sun, Juan Ran, Jianxia Bai and Xiaoye Yang, “Community Detection Based on Individual Topics and Network Topology in Social Networks”, IEEE Access, Vol. 8, pp.124414-124423, 2020.
  [8] Yunlei Zhang, Bin Wu, Nianwen Ning, Chenguang Song, and Jinna Lv, “Dynamic Topical Community Detection in Social Network: A Generative Model Approach”, IEEE Access, Vol. 7, pp.74528-74541, 2019.
  [9] Xiaoming Li, Guangquan Xu, Wenjuan Lian, Hequn Xian, Gangquan Xu, Litao Jiao, and Yu Huang, “Multi-Layer Network Local Community Detection Based on Influence Relation”, IEEE Access Special Section on Security and Privacy in Emerging Decentralized Communication Environments, Vol. 07, pp. 89051-89062, 2019.
  [10] Ling Wu, Qishan Zhang, Chihua Chen, Kun Guo, and Deqin Wang, “Deep Learning Techniques for Community Detection in Social Networks”, IEEE Access Special Section on Data Mining for Internet of Things, Vol. 8, pp.96016-96026, 2020.
  [11] A. Mourad, A. Srour, H. Harmanani, C. Jenainati and M. Arafeh, “Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions”, IEEE Transactions on Network and Service Management, Vol. 17, Issue. 4, pp. 2145-2155, 2020.
  [12] Md Saiful Islam, Tonmoy Sarkar, Sazzad Hossain Khan, Abu-Hena Mostofa Kamal, S. M. Murshid Hasan, Alamgir Kabir, Dalia Yeasmin, Mohammad Ariful Islam, Kamal Ibne Amin Chowdhury, Kazi Selim Anwar, Abrar Ahmad Chughtai, and Holly Seale, “COVID-19 Related Infodemic and Its Impact on Public Health: A Global Social Media Analysis”, American Journal of Tropical medicine and Hygiene, Vol.103, Issue.4, pp.1621-1629, 2020.
  [13] Enrico De Santis, Alessio Martino and Antonello Rizzi, “An Infoveillance System for Detecting and Tracking Relevant Topics from Italian Tweets during the COVID-19 Event”, IEEE Access, Vol. 8, pp.132527-132538, 2020.
  [14] K. Ganasegeran and S. A. Abdulrahman, “Artificial Intelligence Applications in Tracking Health Behaviors During Disease Epidemics”, Cham: Springer International Publishing, Vol.6, Issue.1, pp. 141–155, 2020.
  [15] Z. Hou, F. Du, H. Jiang, X. Zhou, and L. Lin, “Assessment of public attention, risk perception, emotional and behavioural responses to the COVID-19 outbreak”, Social media surveillance in China, Vol.14, Issue.3, pp.1-22, 2020.
  [16] Ly Dinh and Nikolaus Parulian, “COVID-19 pandemic and information diffusion analysis on Twitter”, In the Proceedings of Association of Information Science and Technology, Wiley, Vol. 57, Issue.1, pp.1-10, 2020.
  [17] Sanjay Kumar, Muskan Saini, Muskan Goel and B.S Panda, “Modeling information diffusion in online social networks using a modified forest-fire model”, Journal of Intelligent Information Systems, Springer, Vol. 56, pp.355-377, 2021.
  [18] Haobin Fan and Xuanyi Nie, “Impacts of Layoffs and Government Assistance on Mental Health during COVID-19: An Evidence-Based study of the United States”, Sustainability, Vol.12, Issue. 18, pp.7763, 2020.
  [19] Bukhari and W. Hussain, “Role of Social Media in COVID-19 Pandemic”, International Journal of Frontier Sciences, Vol. 4, Issue. 2, pp. 59-60, 2020.
  [20] Heena Sahni and Hunny Sharma, “Role of social media during the COVID-19 pandemic: Beneficial, destructive, or reconstructive?”, International Journal of Academic Medicine, Vol. 6, No. 2, pp.70-75, 2020.
  [21] Ana Blasco Belled, Claudia Tejada Gallardo, Cristina Torrelles Nadal, and Carles Alsinet, “The Costs of the COVID-19 on Subjective Well-Being: An Analysis of the Outbreak in Spain”, Sustainability, Vol.12, Issue.15, pp.6243, 2020.

  Citation
  Ranjith K., Dhiya K.K., "A Review on Community Detection Algorithms in Social Networks for COVID-19 Related Fake News Detection, Management and Service Recommendation.," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.5, pp.30-37, 2021

• Open Access   Article

  3D Modelling and its Applications in Mechanical Engineering

  Vansh Wahi

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.5 , pp.38-42, Oct-2021

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  Abstract
  Through this research paper, I hope to explain and provide an insight into the field of 3D Modelling and Designing with direct respect to Mechanical Engineering. Different aspects of Designing, in real-world scenarios, as well as their applications have been discussed throughout this paper. 3D CAD modelling and rendering has been described in detail, along with its advances and applications under mechanical engineering and product development. Practical terms and components of the product development process have been defined and explained in detail. Finally, a comprehensive case study into the mechanical aspects of building a F1 car is detailed with relevant applications of the terms discussed.

  Key-Words / Index Term
  Rendering, CAD, Mechanical Modelling, 3D Engine, 3D Modelling

  References
  [1] Fabio Remondino,Sabry El-Hakim, “Image-Based 3D Modelling: A Review”, The Photogrammic Record, Vol 21, Issue 115, 2006
  [2] Rajinder Singh, "3D Game Development Engines and 3D Modelling", International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1047-1053, 2019.
  [3] Catalin Alexandra, “Modeling and Simulation in Engineering”, InTech Publication, pp. 69-85, 2012
  [4] Dick, Anthony R., Philip HS Torr, and Roberto Cipolla. "Automatic 3D Modelling of Architecture." BMVC. Vol. 1, Issue 1, 2000.
  [5] Bayraktat, Ömer, et al. “Experimental study on the 3D?printed plastic parts and predicting the mechanical properties using artificial neural networks”. Polymers for Advanced Technologies, Vol 7, Issue 2, 28.8: pp.1044-1051, 2017.

  Citation
  Vansh Wahi, "3D Modelling and its Applications in Mechanical Engineering," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.5, pp.38-42, 2021

• Open Access   Article

  Performance Analysis of Interservice Communication in Microservices

  Farid Salah, Mohammadreza Eslaminejad, Ali Ghermezian

  Case Study | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.5 , pp.43-47, Oct-2021

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  Abstract
  Microservice architecture is a popular architecture for cloud-native software applications. Microservices are loosely coupled, and each service is responsible for its data. However, when it comes to data retrieval that requires information from different data sources, microservices need to communicate with each other. There are several approaches to address this requirement. In this paper, we implemented two approaches of interservice communication and compared their performance. We have also highlighted benefits and drawbacks of each approach and introduced some alternatives.

  Key-Words / Index Term
  Microservices; Interservice Communication; API Composition; API Gateway

  References
  [1] N. Torvekar and P. S. Game, “Review Paper Microservices and It’s Applications?: An Overview”, Int. J. Comput. Sci. Eng., vol. 7, no. 4, pp. 803–809, 2019.
  [2] S. S. Paradkar, “eGovernment Integration Framework for Fragmented Systems”, Int. J. Comput. Sci. Eng., vol. 9, no. 1, pp. 51–55, 2021.
  [3] H. Chawla and H. Kathuria, “Building Microservices Applications on Microsoft Azure: Designing, Developing, Deploying and Monitoring”, Apress, 2019.
  [4] C. Richards, “Microservices Patterns”, Manning Publications Co., 2018.
  [5] O. Al-Debagy and P. Martinek, “A Comparative Review of Microservices and Monolithic Architectures”, in 18th IEEE International Symposium on Computational Intelligence and Informatics, pp. 149–154, 2018, doi: 10.1109/CINTI.2018.8928192.
  [6] K. Gos and W. Zabierowski, “The Comparison of Microservice and Monolithic Architecture”, in 6th International Conference on the Perspective Technologies and Methods in MEMS Design, pp. 150–153, 2020, doi: 10.1109/MEMSTECH49584.2020.9109514.
  [7] T. Ueda, T. Nakaike, and M. Ohara, “Workload characterization for microservices”, in 2016 IEEE International Symposium on Workload Characterization (IISWC), pp. 1–10, 2016, doi: 10.1109/IISWC.2016.7581269.
  [8] M. S. Hamzehloui, S. Sahibuddin, and K. Salah, “A Systematic Mapping Study on Microservices”, in Recent Trends in Data Science and Soft Computing, pp. 1079–1090, 2019.
  [9] H. Song, P. H. Nguyen, F. Chauvel, J. Glattetre, and T. Schjerpen, “Customizing multi-tenant saas by microservices: A reference architecture”, in 2019 IEEE International Conference on Web Services (ICWS), pp. 446–448, 2019, doi: 10.1109/ICWS.2019.00081.
  [10] T. Cerny, M. J. Donahoo, and J. Pechanec, “Disambiguation and Comparison of SOA, Microservices and Self-Contained Systems”, in RACS ’17: Proceedings of the International Conference on Research in Adaptive and Convergent Systems, pp. 228–235, 2017, doi: 10.2337/dc07-2424.
  [11] B. El Khalyly, A. Belangour, M. Banane, and A. Erraissi, “A comparative study of microservices-based IoT platforms”, Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 8, pp. 389–398, 2020, doi: 10.14569/IJACSA.2020.0110850.
  [12] S. Newman, “Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith”, O’Reilly Media, Inc., 2019.
  [13] M. Gribaudo, M. Iacono, and D. Manini, “Performance evaluation of massively distributed microservices based applications”, in 31st European Conference on Modelling and Simulation, pp. 598–604, 2017, doi: 10.7148/2017-0598.
  [14] R. Flygare and A. Holmqvist, “Performance characteristics between monolithic and microservice-based systems”, Blekinge Institute of Technology, 2017.
  [15] V. Singh and S. K. Peddoju, “Container-based Microservice Architecture for Cloud Applications”, in IEEE International Conference on Computing, Communication and Automation, pp. 847–852, 2017, doi: 10.1109/CCAA.2017.8229914.
  [16] K. Munonye and P. Martinek, “Evaluation of Data Storage Patterns in Microservices Archicture”, in IEEE 15th International Conference of System of Systems Engineering (SoSE), pp. 373–380, 2020,doi: 10.1109/SoSE50414.2020.9130516.
  [17] K. Indrasiri and P. Siriwardena, “Microservices for the Enterprise”, Apress, 2018.

  Citation
  Farid Salah, Mohammadreza Eslaminejad, Ali Ghermezian, "Performance Analysis of Interservice Communication in Microservices," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.5, pp.43-47, 2021

• Open Access   Article

  Development of Smart IoT-Based CNN Technique for Harmful Maize Insects Recognition in Precision Agriculture

  Ezeofor Chukwunazo Joseph

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.5 , pp.48-60, Oct-2021

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  Abstract
  this paper presents Development of Smart Internet of Things (IoT)-Based Convolutional Neural Network (CNN) technique for Harmful Insects Recognition in Precision Agriculture. There has been a reported case of aggressive influx of maize moths (Spodoptera species) in maize farms, which has caused tremendous damage to maize crops yield yearly in Nigeria. Nigerian Farmers are still worried because reliable solutions have not been provided that would eliminate the attack of these moths. The maize moths were captured in the farm using installed delta pheromone traps and laboratory moths’ breeding. The captured images stored in the online Google drive dataset folder for the model training. Android mobile app was designed using Figma Editor and developed in Android studio using Kotlin programming language. The IoT system comprises of Raspberry Pi with in-built sensors for sensing, detecting and capturing of the targeted maize moths in the farm. A custom Convolutional Neural Network, called MothNet model was developed and trained in Google Colab using various Python libraries. During training and validation tests, MothNet achieved more than 90% accuracy with low losses but could not generalize well when tested on site because of the small images in the dataset. In order to ensure robust and generalizable system, transfer learning with data augmentation techniques was used to develop a better-performing model. Residual Neural Network (ResNet 50) Architecture was selected, modified and fine-tuned for the image classifications. During on-site testing, the IoT system was set up in the maize farm and internet connectivity established. Maize moth’s image was captured by the IoT system and used cloud based model to detect the image. The image was sent to the Amazon cloud via secure file transfer protocol (FTP) for classification. The modified ResNet-50 model stored in the cloud was called for prediction which achieved 85% accuracy. The predictions were sent to the Amazon Web Services (AWS) S3 database for storage and maize app used to download the predicted moths from the database for the farmers’ attention.

  Key-Words / Index Term
  Spodoptera species, IoT, Maize, CNN, Google Colab, MothNet, ResNet 50, Python

  References
  [1] P. K. Natikar, R. A. Balikai and Ch. Anusha, “Pest Management Strategies in Precision Farming,” J. Exp. Zool. India Vol. 19, No. 1, pp. 1-8, 2016.
  [2] P. F. Konstantinos,“Deep learning models for plant disease detection and diagnosis,” Computers and Electronics in Agriculture, Vol.145, no.1, pp. 311–318. 2008.
  [3] G. L. Konstantinos, B. Patrizia, M. Dimitrios, P. Simon and B. Dionysis, “Machine Learning in Agriculture: A Review,” Published in: Sensors MDPI, Vol 18 issue 8. pp. 2674, 2018.
  [4] N. N. Esther,“Africa’s most notorious insects – the bugs that hit agriculture the hardest.,” Sept.2017 [Online] available:https://theconversation.com/africas-most-notorious-insects-the-bugs-that-hit-agriculture-the-hardest-83107
  [5] E.S. Biswajit, A. Kowsar, B. Premankur, and C. Amit, “Development of m-sahayak-the innovative Android based application for real time assistance in Indian Agriculture and Health Sectors,” UBICOMM 2012:The sixth International conference on mobile ubiquitious computing, system,services and Technology.
  [6] T. Ankur, L.C. De, K. P. Ravi, and K.S. Rakesh, “Mobile Android Application on Orchid farming based on North Eastern States of India,” Short Communication-National Research Centre for Orchids, Pakyong 737106, Sikkim. Vol.76 No.4 pp.752-756 ,2019,
  [7] P.S. Arockia, B.A Thanga, M. Komal, A.H. Khule, “Fungal Detection in Agricultural Crops Using IoT and SVM,” International Research Journal of Engineering and Technology (IRJET), vol.5 No.2, pp. 872-875, 2018.
  [8] R. T. Ms.Nilam and N. Swati, “Early Disease Detection and Monitoring Large Field of Crop by Using IoT,” International Journal of Computer Science and Information Security (IJCSIS), vol.15 no.10, pp.236-248, 2017.
  [9] G. Demin, S. Quan, H. Bin, and Z.Shuo, “A Framework for Agricultural Pest and Disease Monitoring Based on Internet-of-Things and Unmanned Aerial Vehicles,” published in MDPI sensor, vol.20 issue 5, pp.1-18 , 2020.
  [10] M.V. Manoj, “IoT based Unmanned Aerial Vehicle System for Agriculture Applications,” published in 2018 International conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2018,
  [11] S.C. Madhukar, “Automatic Arial Vehicle Based Pesticides Spraying System for Crops”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), Vol.8 Issue 11, pp. 41-44, 2019.
  [12] H. C. Simon, N. Mayumbo, P. Jackson, M. M. Kabemba, O. Y. N. Philip, H.S. Philemon, “An Application of Machine Learning Algorithms in Automated Identification and Capturing of Fall Armyworm (FAW) Moths in the Field,” Proceedings of the Information and Communication Technology Society of Zambia (ICTSZ) International Conference in ICTS, Lusaka, Zambia, pp.119-124. 2018.
  [13] H. C. Simon, N. Mayumbo, P. Jackson, M. K. Monde, O. Y. N. Philip, H. S. Philemon, “Machine Learning Algorithms for Automated Image Capture and Identification of Fall Armyworm (FAW) Moths,” Zambia Information Communication Technology (ICT) Journal. Vol. 3 No.1, pp.1-4, 2019.
  [14] A. M. A. Belal, and S. A. Samy, “Image-Based Tomato Leaves Diseases Detection Using Deep Learning,” International Journal of Academic Engineering Research (IJAER), vol.2 No.12, pp. 10-16, 2018.
  [15] A. M. A. Belal, B, S. A Sammy., and S. A. Samy, “Plant Seedlings Classification Using Deep Learning,” International Journal of Academic Information Systems Research (IJAISR), vol.3 No.1, pp.7-14, 2019.
  [16] K. S. Arnab , S. Jayeeta, R. Radhika, S. Sachet, D. Subhojit , P. C. Soummyo and N. S. Himadri, “IOT-Based Drone for Improvement of Crop Quality in Agricultural Field,” Published in: 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC). Date of Conference: 8-10 Jan. 2018. Las Vegas, NV, USA.
  [17] F. M. Rodrigo, N. Ibrahim, H. T. Anh Lan, “Precision Agriculture Using Remote Monitoring Systems in Brazil,” Published in: 2017 IEEE Global Humanitarian Technology Conference (GHTC), Date of Conference: 19-22 Oct. 2017. Location: San Jose, CA, USA.
  [18] K. Thenmozhi, & U.R. Srinivasulu, “Crop pest classification based on deep convolutional neural network and transfer learning,” published in Elsevier-Computers and Electronics in Agriculture.vol 164 No 1. 2019.
  [19] K. Thenmozhi and R. U. Srinivasulu, “Machine learning ensemble with image processing for pest identification and classification in field crops”, published in Neural Computing and Applications (springer), 02 January 2021
  [20] M. K Nour eldeen , l. mohamed , N. T. mohamed hamed, “Insect Pests Recognition Based On Deep Transfer Learning Models,” Journal of Theoretical and Applied Information Technology, vol. 98 no 1, pp.60-68, 2020.
  [21] C.J. Ezeofor, , K. Akpado, and A. Ulasi, “Predictive Model For Maize Stem Borers’ Classification In Precision Farming”, International Journal of Artificial Intelligence & Applications (IJAIA) vol.12 no 4, pp.33-49, 2021.
  [22] C.J Ezeofor, E.C. Okafor, K Akpado, and U Zakka. “IOT Architecture for Real Time Maize Stem Borers’ Detection and Capturing in Precision Farming”, Journal of Engineering and Applied Sciences (JEAS), Vol.18 no1, pp.381-392, 2021

  Citation
  Ezeofor Chukwunazo Joseph, "Development of Smart IoT-Based CNN Technique for Harmful Maize Insects Recognition in Precision Agriculture," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.5, pp.48-60, 2021

• Open Access   Article

  Analysis of User Acceptance of the E-Ticketing System in Lagos

  I. Njoku, A.A. Adetunji

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.5 , pp.61-68, Oct-2021

  Abstract

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  Abstract
  The study set out to analyze the user acceptance of the e-ticketing system of Lagos Bus Franchise Scheme (BFS). It examined the relationship between perceived usefulness, perceived ease of use and consumer intention towards the use of e-ticketing systems. The study adopted the Technology Acceptance Model (TAM) and systematic sampling method to carry out the research. Questionnaires were used as the instrument for data collection. The research hypotheses were tested with Chi-square test statistic. The findings of this study revealed that perceived usefulness and perceived ease of use were antecedents to consumer’s intention to use e-ticketing systems. Third statement “I would find the BFS e-ticketing system useful in making bus fare payments” shows that 122 (60.4 %) of respondents agreed with this statement. The study concludes there is need to promote the scheme to develop e-ticketing systems that are easy to use, functional and reliable in order to elicit belief in the system on the part of the user. The paper therefore suggests that, efforts should be made to increase the level of awareness about the use of e-ticketing system as a better way of making bus fare payment within the Bus Franchise Scheme.

  Key-Words / Index Term
  E-ticketing, Model, Technology, Intention, Franchise, Scheme

  References
  [1] R. Abeyratne,“Electronic Ticketing in Air Transport-Commercial Strategies and Consequences”. Journal of World Trade, 2005.
  [2] J. C. Westland, and T. H. K. Clark, “Global Electronic Commerce: Theory and Case Studies”. Cambridge, MA: MIT press, 2000.
  [3] A. K. Nilosey and R. Kharche, “E-Ticketing Through E-Wallet Scheme”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 3, Issue 2, pp.17-20, 2015. Available online at: www.isroset.org
  [4] EMTA, “Study on Electronic Ticketing in Public Transport. European Metropolitan Transport Authorities”, (53), 2008.
  [5] Federal Reserve Bank of Boston “The Contactless Wave: A Case Study in Transit Payments”. Boston, 2008.
  [6] D. I. McCuhbrey,, “Disintermediation and Reintermediation In The U.S. Air Travel Distribution Industry”: A Delphi Study: Communications the Ais, 1:18., 1999.
  [7] S. Taylor and P. A. Todd, “Understanding information technology usage: a test of competing models”. Information systems Research. Vol.6, No. 2, pp.144-176, 1995.
  [8] E. S. Soegoto, R.. Setiawan and R. Jumansyah,“Impact of E-Ticketing Application on Bus Transportation in Bandung”, Advances in Economics, Business and Management Research, International Conference on Business, Economic, Social Science, and Humanities, Indonesia. Vol. 112, pp.25-28, 2019.
  [9] O. B. Ndikom, “Elements of Transport Management: Transport and Development. Lagos:” Bunmico Publishers, 2008.
  [10] K. Adeniji, “Public Transportation and Basic Needs Satisfaction in Nigeria. Research for Development, Vol.4, No.2, pp.292-304, 1987.
  [11] E. A. Toachimsthaler and M. Alavi, “Revisiting DSS Implementation Research: A Meta-Analysis of the Literature and Suggestion for Researchers. MIS Quarterly. Vol.16, No.1, pp.95 116, 1992.
  [12] H. C. Lucas Jr., M. J. Iinberg and R. L. Schultz., “Information Systems Implementation. Testing a Structural Model”, Norwood, NJ: Ablex Publishing Corporation, 1990.
  [13] L. Gitte, “Usability Testing and System Evaluation.” London: Chapman & Hall, 1994.
  [14] A. Dillon and M.G. Morris, “User acceptance of information technology: Theories and models”. Annual Review of Information Science and Technology; 31, pp3-32, 1996.
  [15] G. R. Salancik and J. Pfeffer, “A Social Information Processing Approach to Job Attitudes and Task Design. Administrative Science Quarterly. 23: pp.224-253, 1987.
  [16] J. M. Lubanga, T. Gakobo, I. Ochieng and L. N. Kimando, “Factors Influencing Adoption Of e-Payment System in Kenyan Public Transport: A Case of Matatu Plying Nairobi-Kitengela Route” International Academic Journal of Human Resource and Business Administration, Vol. 2, Issue 4, pp.27-48, 2017.
  [17] F. D. Davis, R. P. Bagozzi and P. R. Warshaw, ”User Acceptance of Computer Technology: A Comparison of Two Theoretical Models”. Journal of Management Science. Vol.35, No.8, pp.982-1003, 1989.
  [18] P. K. T. Marfo,and E. Quansah,“Factors Influencing the Adoption of E-Ticketing System in the Bus Transport Sector in Ghana”. Journal of Software Engineering and Applications, 13, pp.161-178, 2020
  [19] S. Wangpipatwong, W. Chutimaskul and B. Papasraton, “Understanding Citizen’s Continuance Intention to Use e-Government Website: A Composite View of Technology Acceptance Model and Computer Self-Efficacy”. The Electronic Journal Of E-Government. Vol.6, No.1: pp.55-64,.2008.
  [20] S. Hung, C. Chang and L. Yu., “Determinants of User Acceptance of the e-Government Services: The Case of Online Tax Filing and Payment System”. Government Information Quarterly. 23: pp.97 -122, 2006.
  [21] M. Horst, M. Kuttschreuter and J. C. Gutteling, “Perceived Usefulness, Personal Experiences, Risk Perception and Trust as Determinants of Adoption of e-Government Services in the Netherlands”. Computer in Human Behaviour. 23: 1838-1852, 2007.
  [22] P. Guriting and N. O. Ndubisi, “Borneo Online Banking: Evaluating Customer Perceptions and Behavioural Intention”. Management Research News. Vol.29, Issue1/2, pp.6-15, 2006.
  [23] I. Ajzen and M. Fishbein, “Understanding Attitudes and Predicting Social Behavior. Englewood Cliffs, NJ: Prentice-Hall, 1980.
  [24] Y. H. Chen and S. Barnes, “Initial Trust and Online Buyer Behaviour. Industry Management Data System” Vol.107, No.1, pp.21-36, 2007.
  [25] M. Tan and T. S. H. Teo, “Factors influencing the adoption of internet banking”, Journal of the Association Information Systems, Vol.1, No.5, pp.22-38, 2000.
  [26] V. N. Polatoglu and S. Ekin, “An empirical investigation of the Turkish consumers` acceptance of internet banking services”. International Journal of Bank Market, Vol.19, No.4, pp.156-165, 2001.
  [27] E. M. Rogers, “Diffusion of Innovations”, (4th ed.). New York, NY: The Free Press, 1983.
  [28] T. Yamane, “Statistics: An introductory Analysis”, 2nd Ed, New York: Harper and Row, 1967.

  Citation
  I. Njoku, A.A. Adetunji, "Analysis of User Acceptance of the E-Ticketing System in Lagos," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.5, pp.61-68, 2021